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AWS-Certified-Database-Specialty New Guide Files, AWS-Certified

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    NEW QUESTION 31
    A company is building a new web platform where user requests trigger an AWS Lambda function that performs an insert into an Amazon Aurora MySQL DB cluster. Initial tests with less than 10 users on the new platform yielded successful execution and fast response times. However, upon more extensive tests with the actual target of 3,000 concurrent users, Lambda functions are unable to connect to the DB cluster and receive too many connections errors.
    Which of the following will resolve this issue?

    • A. Increase the number of Aurora Replicas
    • B. Increase the instance size of the DB cluster
    • C. Change the DB cluster to Multi-AZ
    • D. Edit the my.cnf file for the DB cluster to increase max_connections

    Answer: B

     

    NEW QUESTION 32
    A company is closing one of its remote data centers. This site runs a 100 TB on-premises data warehouse solution. The company plans to use the AWS Schema Conversion Tool (AWS SCT) and AWS DMS for the migration to AWS. The site network bandwidth is 500 Mbps. A Database Specialist wants to migrate the on- premises data using Amazon S3 as the data lake and Amazon Redshift as the data warehouse. This move must take place during a 2-week period when source systems are shut down for maintenance. The data should stay encrypted at rest and in transit.
    Which approach has the least risk and the highest likelihood of a successful data transfer?

    • A. Set up a VPN tunnel for encrypting data over the network from the data center to AWS. Leverage AWS SCT and apply the converted schema to Amazon Redshift. Once complete, start an AWS DMS task to move the data from the source to Amazon S3. Use AWS Glue to load the data from Amazon S3 to Amazon Redshift.
    • B. Set up a VPN tunnel for encrypting data over the network from the data center to AWS. Leverage a native database export feature to export the data and compress the files. Use the aws S3 cp multi-port upload command to upload these files to Amazon S3 with AWS KMS encryption. Once complete, load the data to Amazon Redshift using AWS Glue.
    • C. Leverage AWS SCT and apply the converted schema to Amazon Redshift. Once complete, use a fleet of
      10 TB dedicated encrypted drives using the AWS Import/Export feature to copy data from on-premises to Amazon S3 with AWS KMS encryption. Use AWS Glue to load the data to Amazon redshift.
    • D. Leverage AWS SCT and apply the converted schema to Amazon Redshift. Start an AWS DMS task with two AWS Snowball Edge devices to copy data from on-premises to Amazon S3 with AWS KMS encryption. Use AWS DMS to finish copying data to Amazon Redshift.

    Answer: D

    Explanation:
    Explanation
    https://aws.amazon.com/blogs/database/new-aws-dms-and-aws-snowball-integration-enables-mass-database-mig

     

    NEW QUESTION 33
    A company is running its customer feedback application on Amazon Aurora MySQL. The company runs a report every day to extract customer feedback, and a team reads the feedback to determine if the customer comments are positive or negative. It sometimes takes days before the company can contact unhappy customers and take corrective measures. The company wants to use machine learning to automate this workflow.
    Which solution meets this requirement with the LEAST amount of effort?

    • A. Export the Aurora MySQL database to Amazon S3 by using AWS Database Migration Service (AWS DMS). Use Amazon SageMaker to run sentiment analysis on the exported files.
    • B. Set up Aurora native integration with Amazon Comprehend. Use SQL functions to extract sentiment analysis.
    • C. Set up Aurora native integration with Amazon SageMaker. Use SQL functions to extract sentiment analysis.
    • D. Export the Aurora MySQL database to Amazon S3 by using AWS Database Migration Service (AWS DMS). Use Amazon Comprehend to run sentiment analysis on the exported files.

    Answer: B

    Explanation:
    For details about using Aurora and Amazon Comprehend together, see Using Amazon Comprehend for sentiment detection. Aurora machine learning uses a highly optimized integration between the Aurora database and the AWS machine learning (ML) services SageMaker and Amazon Comprehend.
    https://www.stackovercloud.com/2019/11/27/new-for-amazon-aurora-use-machine-learning-directly-from-your-databases/

     

    NEW QUESTION 34
    A database professional is tasked with the task of migrating 25 GB of data files from an on-premises storage system to an Amazon Neptune database.
    Which method of data loading is the FASTEST?

    • A. Upload the data to Amazon S3 and use the Loader command to load the data from Amazon S3 into the Neptune database.
    • B. Use the AWS CLI to load the data directly from the on-premises storage into the Neptune database.
    • C. Use AWS DataSync to load the data directly from the on-premises storage into the Neptune database.
    • D. Write a utility to read the data from the on-premises storage and run INSERT statements in a loop to load the data into the Neptune database.

    Answer: A

    Explanation:
    Explanation
    1.Copy the data files to an Amazon Simple Storage Service (Amazon S3) bucket.
    2. Create an IAM role with Read and List access to the bucket.
    3. Create an Amazon S3 VPC endpoint.
    4. Start the Neptune loader by sending a request via HTTP to the Neptune DB instance.
    5. The Neptune DB instance assumes the IAM role to load the data from the bucket.

     

    NEW QUESTION 35
    ......